<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Human daily activity segmentation utilizing smartphone sensing technology is quite new challenge. In this paper, the segmentation method combining statistical model and time series analysis is designed and implemented. According to designed partition procedure, real measured accelerometer datasets of human daily activities are tested. The segmentation performance of sliding window autocorrelation and minimized contrast algorithms is analysed and compared. Experiments demonstrate the effectiveness of this proposed automatic human activity separation method focusing on the application of mobile sensor. As the properties of signal, mean, variance, frequency and amplitude are all useful features on the case of motion sensor-based human daily activity segmentation. In the end, the suggested work to improve the developed partition model is presented.</p></abstract>

Human daily activity segmentation utilizing smartphone sensing technology is quite new challenge. In this paper, the segmentation method combining statistical model and time series analysis is designed and implemented. According to designed partition procedure, real measured accelerometer datasets of human daily activities are tested. The segmentation performance of sliding window autocorrelation and minimized contrast algorithms is analysed and compared. Experiments demonstrate the effectiveness of this proposed automatic human activity separation method focusing on the application of mobile sensor. As the properties of signal, mean, variance, frequency and amplitude are all useful features on the case of motion sensor-based human daily activity segmentation. In the end, the suggested work to improve the developed partition model is presented.

Automatic Human Daily Activity Segmentation Applying Smart Sensing Technology

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Automatic Human Daily Activity Segmentation Applying Smart Sensing Technology"}

Human daily activity segmentation utilizing smartphone sensing technology is quite new challenge. In this paper, the segmentation method combining statistical...

Automatic Human Daily Activity Segmentation Applying Smart Sensing Technology

Data publikacji: 01 wrz 2015

Zakres stron: 1624 - 1640

Otrzymano: 08 kwi 2015

Przyjęty: 21 lip 2015

DOI: https://doi.org/10.21307/ijssis-2017-822

© 2015 Yin Ling et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.